{"id":"https://openalex.org/W2930037515","doi":"https://doi.org/10.1109/icassp.2019.8683374","title":"Bayesian Non-parametric Multi-source Modelling Based Determined Blind Source Separation","display_name":"Bayesian Non-parametric Multi-source Modelling Based Determined Blind Source Separation","publication_year":2019,"publication_date":"2019-04-17","ids":{"openalex":"https://openalex.org/W2930037515","doi":"https://doi.org/10.1109/icassp.2019.8683374","mag":"2930037515"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2019.8683374","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2019.8683374","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5015372147","display_name":"Chaitanya Narisetty","orcid":"https://orcid.org/0000-0002-9239-5534"},"institutions":[{"id":"https://openalex.org/I118347220","display_name":"NEC (Japan)","ror":"https://ror.org/04jndar25","country_code":"JP","type":"company","lineage":["https://openalex.org/I118347220"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Chaitanya Narisetty","raw_affiliation_strings":["Data Science Research Laboratories, NEC Corporation, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Data Science Research Laboratories, NEC Corporation, Japan","institution_ids":["https://openalex.org/I118347220"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113730169","display_name":"Tatsuya Komatsu","orcid":null},"institutions":[{"id":"https://openalex.org/I118347220","display_name":"NEC (Japan)","ror":"https://ror.org/04jndar25","country_code":"JP","type":"company","lineage":["https://openalex.org/I118347220"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tatsuya Komatsu","raw_affiliation_strings":["Data Science Research Laboratories, NEC Corporation, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Data Science Research Laboratories, NEC Corporation, Japan","institution_ids":["https://openalex.org/I118347220"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041279908","display_name":"Reishi Kondo","orcid":null},"institutions":[{"id":"https://openalex.org/I118347220","display_name":"NEC (Japan)","ror":"https://ror.org/04jndar25","country_code":"JP","type":"company","lineage":["https://openalex.org/I118347220"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Reishi Kondo","raw_affiliation_strings":["Data Science Research Laboratories, NEC Corporation, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Data Science Research Laboratories, NEC Corporation, Japan","institution_ids":["https://openalex.org/I118347220"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I118347220"],"apc_list":null,"apc_paid":null,"fwci":0.5009,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.61689695,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"111","last_page":"115"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9941999912261963,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9485999941825867,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/non-negative-matrix-factorization","display_name":"Non-negative matrix factorization","score":0.7723031044006348},{"id":"https://openalex.org/keywords/blind-signal-separation","display_name":"Blind signal separation","score":0.7245928645133972},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6772399544715881},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.6723684668540955},{"id":"https://openalex.org/keywords/source-separation","display_name":"Source separation","score":0.648270845413208},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.6477670669555664},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.5621506571769714},{"id":"https://openalex.org/keywords/parametric-model","display_name":"Parametric model","score":0.542169988155365},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5040868520736694},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4562768042087555},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42663419246673584},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.4115039110183716},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3333175778388977},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.26457154750823975},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.15980195999145508}],"concepts":[{"id":"https://openalex.org/C152671427","wikidata":"https://www.wikidata.org/wiki/Q10843505","display_name":"Non-negative matrix factorization","level":4,"score":0.7723031044006348},{"id":"https://openalex.org/C120317606","wikidata":"https://www.wikidata.org/wiki/Q17105967","display_name":"Blind signal separation","level":3,"score":0.7245928645133972},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6772399544715881},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.6723684668540955},{"id":"https://openalex.org/C2776864781","wikidata":"https://www.wikidata.org/wiki/Q52617913","display_name":"Source separation","level":2,"score":0.648270845413208},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.6477670669555664},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.5621506571769714},{"id":"https://openalex.org/C24574437","wikidata":"https://www.wikidata.org/wiki/Q7135228","display_name":"Parametric model","level":3,"score":0.542169988155365},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5040868520736694},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4562768042087555},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42663419246673584},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.4115039110183716},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3333175778388977},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26457154750823975},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.15980195999145508},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.0},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2019.8683374","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2019.8683374","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W176887922","https://openalex.org/W1516111018","https://openalex.org/W1755563775","https://openalex.org/W2046134527","https://openalex.org/W2072548008","https://openalex.org/W2088881802","https://openalex.org/W2097242476","https://openalex.org/W2099741732","https://openalex.org/W2112050062","https://openalex.org/W2113526703","https://openalex.org/W2114461480","https://openalex.org/W2117111086","https://openalex.org/W2127851351","https://openalex.org/W2128925311","https://openalex.org/W2138309709","https://openalex.org/W2141061845","https://openalex.org/W2166851633","https://openalex.org/W2168273590","https://openalex.org/W2245569228","https://openalex.org/W2412956798","https://openalex.org/W2765561170","https://openalex.org/W2771132365","https://openalex.org/W2903518051","https://openalex.org/W2916985722","https://openalex.org/W3099640513","https://openalex.org/W4294562888","https://openalex.org/W4388257489","https://openalex.org/W6607207477","https://openalex.org/W6676519436","https://openalex.org/W6677017536","https://openalex.org/W6684578138","https://openalex.org/W6690610466"],"related_works":["https://openalex.org/W2351387116","https://openalex.org/W2361100278","https://openalex.org/W1658347130","https://openalex.org/W2547262076","https://openalex.org/W2398836525","https://openalex.org/W2563421448","https://openalex.org/W1565566036","https://openalex.org/W1509813908","https://openalex.org/W2774154397","https://openalex.org/W2921513691"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"a":[3,21,38,65],"determined":[4],"blind":[5],"source":[6,16,85],"separation":[7],"method":[8,81],"using":[9,30],"Bayesian":[10,66],"non-parametric":[11,67],"modelling":[12,28],"of":[13,24,58],"sources.":[14],"Conventionally":[15],"signals":[17,26],"are":[18],"separated":[19],"from":[20],"given":[22],"set":[23],"mixture":[25],"by":[27],"them":[29],"non-negative":[31],"matrix":[32],"factorization":[33],"(NMF).":[34],"However":[35],"in":[36],"NMF,":[37],"latent":[39,74],"variable":[40],"signifying":[41],"model":[42],"complexity":[43],"must":[44],"be":[45,57],"appropriately":[46],"specified":[47],"to":[48,72,83],"avoid":[49],"over-fitting":[50],"or":[51],"under-fitting.":[52],"As":[53],"real-world":[54],"sources":[55],"can":[56],"varying":[59],"and":[60],"unknown":[61],"complexities,":[62,86],"we":[63],"propose":[64],"framework":[68],"which":[69],"is":[70],"invariant":[71],"such":[73],"variables.":[75],"We":[76],"show":[77],"that":[78],"our":[79],"proposed":[80],"adapts":[82],"different":[84],"while":[87],"conventional":[88],"methods":[89],"require":[90],"parameter":[91],"tuning":[92],"for":[93],"optimal":[94],"separation.":[95]},"counts_by_year":[{"year":2021,"cited_by_count":3}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}